Overview

Brought to you by YData

Dataset statistics

Number of variables16
Number of observations2156
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory269.6 KiB
Average record size in memory128.1 B

Variable types

Numeric16

Alerts

feat_net_attrition is highly overall correlated with feat_passive_renewalHigh correlation
feat_passive_renewal is highly overall correlated with feat_net_attrition and 3 other fieldsHigh correlation
feat_pct_FPL_100_150 is highly overall correlated with feat_passive_renewal and 6 other fieldsHigh correlation
feat_pct_FPL_150_250 is highly overall correlated with feat_pct_FPL_100_150 and 2 other fieldsHigh correlation
feat_pct_FPL_250_400 is highly overall correlated with feat_passive_renewal and 6 other fieldsHigh correlation
feat_pct_FPL_400_plus is highly overall correlated with feat_passive_renewal and 5 other fieldsHigh correlation
feat_pct_asian is highly overall correlated with feat_pct_blackHigh correlation
feat_pct_black is highly overall correlated with feat_pct_FPL_100_150 and 2 other fieldsHigh correlation
feat_pct_bronze is highly overall correlated with feat_pct_FPL_100_150 and 2 other fieldsHigh correlation
feat_subsidy_coverage_ratio is highly overall correlated with feat_pct_FPL_100_150 and 2 other fieldsHigh correlation
feat_pct_bronze has 167 (7.7%) zerosZeros
feat_pct_gold has 578 (26.8%) zerosZeros
feat_pct_FPL_100_150 has 45 (2.1%) zerosZeros
feat_pct_FPL_250_400 has 34 (1.6%) zerosZeros
feat_pct_FPL_400_plus has 180 (8.3%) zerosZeros
feat_net_attrition has 548 (25.4%) zerosZeros
feat_market_newness has 38 (1.8%) zerosZeros
feat_passive_renewal has 41 (1.9%) zerosZeros
feat_pct_black has 1128 (52.3%) zerosZeros
feat_pct_hispanic has 672 (31.2%) zerosZeros
feat_pct_asian has 985 (45.7%) zerosZeros
feat_pct_aian has 1691 (78.4%) zerosZeros

Reproduction

Analysis started2026-02-15 20:31:34.358502
Analysis finished2026-02-15 20:31:54.067380
Duration19.71 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

feat_pct_bronze
Real number (ℝ)

High correlation  Zeros 

Distinct1969
Distinct (%)91.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.474937
Minimum0
Maximum92.349727
Zeros167
Zeros (%)7.7%
Negative0
Negative (%)0.0%
Memory size17.0 KiB
2026-02-15T12:31:54.177290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q126.423593
median36.548753
Q347.128258
95-th percentile60.346278
Maximum92.349727
Range92.349727
Interquartile range (IQR)20.704664

Descriptive statistics

Standard deviation16.899299
Coefficient of variation (CV)0.47637291
Kurtosis-0.14997117
Mean35.474937
Median Absolute Deviation (MAD)10.335416
Skewness-0.35678623
Sum76483.965
Variance285.58631
MonotonicityNot monotonic
2026-02-15T12:31:54.325122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0167
 
7.7%
503
 
0.1%
29.411764712
 
0.1%
34.254143652
 
0.1%
46.327683622
 
0.1%
35.294117652
 
0.1%
33.333333332
 
0.1%
16.666666672
 
0.1%
252
 
0.1%
55.299539172
 
0.1%
Other values (1959)1970
91.4%
ValueCountFrequency (%)
0167
7.7%
2.6330690831
 
< 0.1%
3.0498016311
 
< 0.1%
3.6857419981
 
< 0.1%
5.0795279631
 
< 0.1%
5.2158273381
 
< 0.1%
5.2828062271
 
< 0.1%
5.2937415641
 
< 0.1%
5.7061340941
 
< 0.1%
6.2689585441
 
< 0.1%
ValueCountFrequency (%)
92.349726781
< 0.1%
80.219780221
< 0.1%
78.571428571
< 0.1%
77.66749381
< 0.1%
77.083333331
< 0.1%
76.543209881
< 0.1%
76.521739131
< 0.1%
76.249298151
< 0.1%
75.993091541
< 0.1%
75.813008131
< 0.1%

feat_pct_gold
Real number (ℝ)

Zeros 

Distinct1556
Distinct (%)72.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.539828
Minimum0
Maximum82.352941
Zeros578
Zeros (%)26.8%
Negative0
Negative (%)0.0%
Memory size17.0 KiB
2026-02-15T12:31:54.426983image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5.4375943
Q318.542936
95-th percentile48.291614
Maximum82.352941
Range82.352941
Interquartile range (IQR)18.542936

Descriptive statistics

Standard deviation15.902447
Coefficient of variation (CV)1.2681551
Kurtosis0.88815553
Mean12.539828
Median Absolute Deviation (MAD)5.4375943
Skewness1.394194
Sum27035.87
Variance252.88783
MonotonicityNot monotonic
2026-02-15T12:31:54.550205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0578
 
26.8%
3.8461538463
 
0.1%
8.3333333333
 
0.1%
18.752
 
0.1%
17.391304352
 
0.1%
7.5376884422
 
0.1%
39.473684212
 
0.1%
4.1666666672
 
0.1%
41.509433962
 
0.1%
9.4202898552
 
0.1%
Other values (1546)1558
72.3%
ValueCountFrequency (%)
0578
26.8%
0.40050590221
 
< 0.1%
0.40893822111
 
< 0.1%
0.43352601161
 
< 0.1%
0.451
 
< 0.1%
0.51533742331
 
< 0.1%
0.51712992891
 
< 0.1%
0.54157374961
 
< 0.1%
0.58975304091
 
< 0.1%
0.62615101291
 
< 0.1%
ValueCountFrequency (%)
82.352941181
< 0.1%
64.970930231
< 0.1%
64.878048781
< 0.1%
64.655172411
< 0.1%
64.091858041
< 0.1%
63.278688521
< 0.1%
62.647325481
< 0.1%
62.556488061
< 0.1%
62.068965521
< 0.1%
60.913705581
< 0.1%

feat_pct_FPL_100_150
Real number (ℝ)

High correlation  Zeros 

Distinct2094
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.460193
Minimum0
Maximum91.269841
Zeros45
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size17.0 KiB
2026-02-15T12:31:54.655566image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9.6316029
Q123.017083
median41.355215
Q356.352709
95-th percentile71.4889
Maximum91.269841
Range91.269841
Interquartile range (IQR)33.335626

Descriptive statistics

Standard deviation20.001801
Coefficient of variation (CV)0.49435752
Kurtosis-0.91260703
Mean40.460193
Median Absolute Deviation (MAD)16.781785
Skewness-0.018822769
Sum87232.176
Variance400.07203
MonotonicityNot monotonic
2026-02-15T12:31:55.117698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
045
 
2.1%
16.666666673
 
0.1%
503
 
0.1%
38.709677422
 
0.1%
25.581395352
 
0.1%
39.130434782
 
0.1%
43.902439022
 
0.1%
40.370370372
 
0.1%
18.461538462
 
0.1%
53.926701572
 
0.1%
Other values (2084)2091
97.0%
ValueCountFrequency (%)
045
2.1%
3.4666448121
 
< 0.1%
3.6876355751
 
< 0.1%
4.3478260871
 
< 0.1%
4.3494849291
 
< 0.1%
4.3692741371
 
< 0.1%
4.5714285711
 
< 0.1%
4.7093723931
 
< 0.1%
4.9001814881
 
< 0.1%
4.9474335191
 
< 0.1%
ValueCountFrequency (%)
91.269841271
< 0.1%
85.714285711
< 0.1%
85.063045591
< 0.1%
851
< 0.1%
84.706331051
< 0.1%
84.642565911
< 0.1%
84.313725491
< 0.1%
84.214618971
< 0.1%
83.894389441
< 0.1%
83.68098161
< 0.1%

feat_pct_FPL_150_250
Real number (ℝ)

High correlation 

Distinct2107
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.587778
Minimum0
Maximum58.244681
Zeros15
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size17.0 KiB
2026-02-15T12:31:55.184188image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile18.957775
Q125.97829
median31.017152
Q335.270805
95-th percentile41.467516
Maximum58.244681
Range58.244681
Interquartile range (IQR)9.2925155

Descriptive statistics

Standard deviation7.2132163
Coefficient of variation (CV)0.23582022
Kurtosis1.6862595
Mean30.587778
Median Absolute Deviation (MAD)4.6131903
Skewness-0.47070857
Sum65947.248
Variance52.030489
MonotonicityNot monotonic
2026-02-15T12:31:55.250891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
015
 
0.7%
37.54
 
0.2%
253
 
0.1%
34.090909093
 
0.1%
32.727272733
 
0.1%
33.333333332
 
0.1%
43.752
 
0.1%
33.333333332
 
0.1%
402
 
0.1%
37.531486152
 
0.1%
Other values (2097)2118
98.2%
ValueCountFrequency (%)
015
0.7%
8.4384093111
 
< 0.1%
10.280373831
 
< 0.1%
10.626136661
 
< 0.1%
11.265164641
 
< 0.1%
11.776315791
 
< 0.1%
12.367491171
 
< 0.1%
12.739273931
 
< 0.1%
12.814645311
 
< 0.1%
13.333333331
 
< 0.1%
ValueCountFrequency (%)
58.244680851
< 0.1%
57.264957261
< 0.1%
55.555555562
0.1%
54.385964911
< 0.1%
51.33689841
< 0.1%
50.773993811
< 0.1%
501
< 0.1%
49.707602341
< 0.1%
48.986486491
< 0.1%
48.547717841
< 0.1%

feat_pct_FPL_250_400
Real number (ℝ)

High correlation  Zeros 

Distinct2103
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.506121
Minimum0
Maximum44.308231
Zeros34
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size17.0 KiB
2026-02-15T12:31:55.334396image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.1079086
Q110.286275
median15.696092
Q324.868
95-th percentile33.902183
Maximum44.308231
Range44.308231
Interquartile range (IQR)14.581725

Descriptive statistics

Standard deviation9.3736499
Coefficient of variation (CV)0.53544984
Kurtosis-0.66361069
Mean17.506121
Median Absolute Deviation (MAD)6.724898
Skewness0.39974455
Sum37743.197
Variance87.865312
MonotonicityNot monotonic
2026-02-15T12:31:55.417629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
034
 
1.6%
12.53
 
0.1%
16.42
 
0.1%
17.989417992
 
0.1%
28.205128212
 
0.1%
202
 
0.1%
15.789473682
 
0.1%
17.341040462
 
0.1%
17.948717952
 
0.1%
252
 
0.1%
Other values (2093)2103
97.5%
ValueCountFrequency (%)
034
1.6%
0.73677160081
 
< 0.1%
0.99160945841
 
< 0.1%
1.0232558141
 
< 0.1%
1.0744985671
 
< 0.1%
1.2163656471
 
< 0.1%
1.24141961
 
< 0.1%
1.3590649631
 
< 0.1%
1.3987730061
 
< 0.1%
1.5060240961
 
< 0.1%
ValueCountFrequency (%)
44.308231171
< 0.1%
44.031830241
< 0.1%
43.062200961
< 0.1%
42.792792791
< 0.1%
42.777777781
< 0.1%
42.587064681
< 0.1%
41.925465841
< 0.1%
41.605839421
< 0.1%
41.496598641
< 0.1%
41.212121211
< 0.1%

feat_pct_FPL_400_plus
Real number (ℝ)

High correlation  Zeros 

Distinct1946
Distinct (%)90.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.6924398
Minimum0
Maximum35.771065
Zeros180
Zeros (%)8.3%
Negative0
Negative (%)0.0%
Memory size17.0 KiB
2026-02-15T12:31:55.519288image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.9923643
median6.0227039
Q311.642186
95-th percentile19.351049
Maximum35.771065
Range35.771065
Interquartile range (IQR)8.6498217

Descriptive statistics

Standard deviation6.0657756
Coefficient of variation (CV)0.78853728
Kurtosis0.32611168
Mean7.6924398
Median Absolute Deviation (MAD)3.824336
Skewness0.8915878
Sum16584.9
Variance36.793633
MonotonicityNot monotonic
2026-02-15T12:31:55.591607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0180
 
8.3%
12.54
 
0.2%
13.333333333
 
0.1%
18.181818183
 
0.1%
15.062761512
 
0.1%
4.3795620442
 
0.1%
6.016597512
 
0.1%
5.7017543862
 
0.1%
102
 
0.1%
14.802631582
 
0.1%
Other values (1936)1954
90.6%
ValueCountFrequency (%)
0180
8.3%
0.26288885641
 
< 0.1%
0.27403035411
 
< 0.1%
0.35335689051
 
< 0.1%
0.35519772671
 
< 0.1%
0.4158964881
 
< 0.1%
0.4961240311
 
< 0.1%
0.49689440991
 
< 0.1%
0.50340538941
 
< 0.1%
0.50629624821
 
< 0.1%
ValueCountFrequency (%)
35.771065181
< 0.1%
34.832100171
< 0.1%
31.751824821
< 0.1%
30.732666781
< 0.1%
28.604031951
< 0.1%
26.959745761
< 0.1%
26.946107781
< 0.1%
26.470588241
< 0.1%
26.327193931
< 0.1%
26.16690241
< 0.1%

avg_premium_before_APTC
Real number (ℝ)

Distinct575
Distinct (%)26.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean709.69759
Minimum414
Maximum1483
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.0 KiB
2026-02-15T12:31:55.652857image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum414
5-th percentile532.75
Q1609
median669.5
Q3758.25
95-th percentile1077.25
Maximum1483
Range1069
Interquartile range (IQR)149.25

Descriptive statistics

Standard deviation160.22507
Coefficient of variation (CV)0.22576527
Kurtosis3.4763623
Mean709.69759
Median Absolute Deviation (MAD)70.5
Skewness1.7179103
Sum1530108
Variance25672.072
MonotonicityNot monotonic
2026-02-15T12:31:55.734249image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59822
 
1.0%
61219
 
0.9%
59916
 
0.7%
66615
 
0.7%
60514
 
0.6%
61114
 
0.6%
62213
 
0.6%
71813
 
0.6%
64413
 
0.6%
62413
 
0.6%
Other values (565)2004
92.9%
ValueCountFrequency (%)
4141
< 0.1%
4201
< 0.1%
4271
< 0.1%
4371
< 0.1%
4411
< 0.1%
4441
< 0.1%
4481
< 0.1%
4491
< 0.1%
4511
< 0.1%
4521
< 0.1%
ValueCountFrequency (%)
14831
< 0.1%
14591
< 0.1%
14171
< 0.1%
14141
< 0.1%
13971
< 0.1%
13881
< 0.1%
13701
< 0.1%
13431
< 0.1%
13211
< 0.1%
13121
< 0.1%

feat_subsidy_coverage_ratio
Real number (ℝ)

High correlation 

Distinct2085
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.84571797
Minimum0.44936709
Maximum0.99642218
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.0 KiB
2026-02-15T12:31:55.801696image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.44936709
5-th percentile0.70203395
Q10.79613036
median0.85822403
Q30.91104049
95-th percentile0.9446895
Maximum0.99642218
Range0.54705509
Interquartile range (IQR)0.11491013

Descriptive statistics

Standard deviation0.080799891
Coefficient of variation (CV)0.095539995
Kurtosis1.0229764
Mean0.84571797
Median Absolute Deviation (MAD)0.056023591
Skewness-0.96090577
Sum1823.3679
Variance0.0065286223
MonotonicityNot monotonic
2026-02-15T12:31:55.867669image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.85
 
0.2%
0.81254
 
0.2%
0.8754
 
0.2%
0.83333333334
 
0.2%
0.92913385833
 
0.1%
0.9148936172
 
0.1%
0.88588588592
 
0.1%
0.81117824772
 
0.1%
0.79714738512
 
0.1%
0.91625615762
 
0.1%
Other values (2075)2126
98.6%
ValueCountFrequency (%)
0.44936708861
< 0.1%
0.50325379611
< 0.1%
0.51528384281
< 0.1%
0.51774530271
< 0.1%
0.52653927811
< 0.1%
0.52967032971
< 0.1%
0.5308848081
< 0.1%
0.53222453221
< 0.1%
0.54251012151
< 0.1%
0.54960629921
< 0.1%
ValueCountFrequency (%)
0.99642218251
< 0.1%
0.97775175641
< 0.1%
0.9760225671
< 0.1%
0.97516556291
< 0.1%
0.97432239661
< 0.1%
0.97231270361
< 0.1%
0.97096188751
< 0.1%
0.97024793391
< 0.1%
0.97017543861
< 0.1%
0.96774193551
< 0.1%

feat_net_attrition
Real number (ℝ)

High correlation  Zeros 

Distinct1577
Distinct (%)73.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.1208803
Minimum0
Maximum100
Zeros548
Zeros (%)25.4%
Negative0
Negative (%)0.0%
Memory size17.0 KiB
2026-02-15T12:31:55.967463image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3.5463869
Q38.0926698
95-th percentile17.260227
Maximum100
Range100
Interquartile range (IQR)8.0926698

Descriptive statistics

Standard deviation10.633021
Coefficient of variation (CV)1.7371719
Kurtosis37.981
Mean6.1208803
Median Absolute Deviation (MAD)3.5463869
Skewness5.3642011
Sum13196.618
Variance113.06114
MonotonicityNot monotonic
2026-02-15T12:31:56.067464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0548
 
25.4%
5015
 
0.7%
10012
 
0.6%
22
 
0.1%
3.7313432842
 
0.1%
3.1252
 
0.1%
52
 
0.1%
2.9036004652
 
0.1%
4.1666666672
 
0.1%
3.4482758622
 
0.1%
Other values (1567)1567
72.7%
ValueCountFrequency (%)
0548
25.4%
0.01355013551
 
< 0.1%
0.024260067931
 
< 0.1%
0.031133250311
 
< 0.1%
0.038051750381
 
< 0.1%
0.056609114071
 
< 0.1%
0.061709348971
 
< 0.1%
0.06258801441
 
< 0.1%
0.067537145431
 
< 0.1%
0.069444444441
 
< 0.1%
ValueCountFrequency (%)
10012
0.6%
63.483146071
 
< 0.1%
63.333333331
 
< 0.1%
62.337662341
 
< 0.1%
61.194029851
 
< 0.1%
61.046511631
 
< 0.1%
60.526315791
 
< 0.1%
59.890109891
 
< 0.1%
59.230769231
 
< 0.1%
59.090909091
 
< 0.1%

feat_market_newness
Real number (ℝ)

Zeros 

Distinct2065
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.437474
Minimum0
Maximum50
Zeros38
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size17.0 KiB
2026-02-15T12:31:56.173238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile9.8424361
Q113.206105
median15.349377
Q317.813738
95-th percentile21.944257
Maximum50
Range50
Interquartile range (IQR)4.6076328

Descriptive statistics

Standard deviation4.1214823
Coefficient of variation (CV)0.26697907
Kurtosis5.1237294
Mean15.437474
Median Absolute Deviation (MAD)2.2797891
Skewness-0.20061585
Sum33283.193
Variance16.986616
MonotonicityNot monotonic
2026-02-15T12:31:56.259507image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
038
 
1.8%
16.666666674
 
0.2%
204
 
0.2%
16.923076923
 
0.1%
14.285714293
 
0.1%
12.53
 
0.1%
18.518518522
 
0.1%
17.628205132
 
0.1%
14.566929132
 
0.1%
15.238095242
 
0.1%
Other values (2055)2093
97.1%
ValueCountFrequency (%)
038
1.8%
5.2471018911
 
< 0.1%
6.0377358491
 
< 0.1%
6.2962962961
 
< 0.1%
6.5502183411
 
< 0.1%
6.5546218491
 
< 0.1%
6.5727699531
 
< 0.1%
6.8009340341
 
< 0.1%
6.9246435851
 
< 0.1%
7.002801121
 
< 0.1%
ValueCountFrequency (%)
501
< 0.1%
39.285714291
< 0.1%
31.958762891
< 0.1%
30.029154521
< 0.1%
28.880866431
< 0.1%
27.327327331
< 0.1%
27.184466021
< 0.1%
27.085927771
< 0.1%
26.947166431
< 0.1%
26.282051281
< 0.1%

feat_passive_renewal
Real number (ℝ)

High correlation  Zeros 

Distinct2090
Distinct (%)96.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.090822
Minimum0
Maximum82.426778
Zeros41
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size17.0 KiB
2026-02-15T12:31:56.334238image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile23.30774
Q138.565982
median49.464953
Q357.107662
95-th percentile66.18485
Maximum82.426778
Range82.426778
Interquartile range (IQR)18.54168

Descriptive statistics

Standard deviation14.080241
Coefficient of variation (CV)0.29900181
Kurtosis1.0227441
Mean47.090822
Median Absolute Deviation (MAD)9.0449832
Skewness-0.85233261
Sum101527.81
Variance198.25319
MonotonicityNot monotonic
2026-02-15T12:31:56.418053image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
041
 
1.9%
506
 
0.3%
404
 
0.2%
33.333333333
 
0.1%
302
 
0.1%
44.578313252
 
0.1%
342
 
0.1%
53.333333332
 
0.1%
63.733333332
 
0.1%
45.833333332
 
0.1%
Other values (2080)2090
96.9%
ValueCountFrequency (%)
041
1.9%
7.5187969921
 
< 0.1%
9.2544987151
 
< 0.1%
11.375661381
 
< 0.1%
11.799410031
 
< 0.1%
11.867088611
 
< 0.1%
11.90476191
 
< 0.1%
13.217213111
 
< 0.1%
13.492063491
 
< 0.1%
13.618677041
 
< 0.1%
ValueCountFrequency (%)
82.426778241
< 0.1%
82.275711161
< 0.1%
77.209302331
< 0.1%
76.795580111
< 0.1%
76.56251
< 0.1%
74.795081971
< 0.1%
74.694644531
< 0.1%
74.096385541
< 0.1%
73.449131511
< 0.1%
73.310485171
< 0.1%

feat_pct_age_55_over
Real number (ℝ)

Distinct2106
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.858555
Minimum0
Maximum59.701493
Zeros11
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size17.0 KiB
2026-02-15T12:31:56.500791image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile19.217822
Q123.375755
median26.686989
Q331.552538
95-th percentile40.39993
Maximum59.701493
Range59.701493
Interquartile range (IQR)8.1767827

Descriptive statistics

Standard deviation7.0167887
Coefficient of variation (CV)0.25187195
Kurtosis2.3119517
Mean27.858555
Median Absolute Deviation (MAD)3.8061095
Skewness0.6336414
Sum60063.044
Variance49.235323
MonotonicityNot monotonic
2026-02-15T12:31:56.617471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
011
 
0.5%
33.333333336
 
0.3%
23.809523813
 
0.1%
30.769230773
 
0.1%
36.363636363
 
0.1%
403
 
0.1%
40.09216592
 
0.1%
20.588235292
 
0.1%
26.81159422
 
0.1%
28.917910452
 
0.1%
Other values (2096)2119
98.3%
ValueCountFrequency (%)
011
0.5%
9.2903973951
 
< 0.1%
11.417058431
 
< 0.1%
11.639185261
 
< 0.1%
11.903617771
 
< 0.1%
12.51
 
< 0.1%
12.864916871
 
< 0.1%
13.167155431
 
< 0.1%
13.333333331
 
< 0.1%
13.41560921
 
< 0.1%
ValueCountFrequency (%)
59.701492541
< 0.1%
58.09523811
< 0.1%
56.884875851
< 0.1%
56.086142321
< 0.1%
54.961832061
< 0.1%
54.922894421
< 0.1%
54.029850751
< 0.1%
53.642384111
< 0.1%
53.333333331
< 0.1%
52.779893371
< 0.1%

feat_pct_black
Real number (ℝ)

High correlation  Zeros 

Distinct1028
Distinct (%)47.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.474265
Minimum0
Maximum38.028169
Zeros1128
Zeros (%)52.3%
Negative0
Negative (%)0.0%
Memory size17.0 KiB
2026-02-15T12:31:56.700851image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32.877449
95-th percentile12.059824
Maximum38.028169
Range38.028169
Interquartile range (IQR)2.877449

Descriptive statistics

Standard deviation4.5664365
Coefficient of variation (CV)1.845573
Kurtosis9.6068843
Mean2.474265
Median Absolute Deviation (MAD)0
Skewness2.758765
Sum5334.5152
Variance20.852343
MonotonicityNot monotonic
2026-02-15T12:31:56.788540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01128
52.3%
0.57986294152
 
0.1%
11.661585371
 
< 0.1%
6.080402011
 
< 0.1%
0.85372794541
 
< 0.1%
1.2381505131
 
< 0.1%
0.41808204861
 
< 0.1%
3.897099071
 
< 0.1%
1.8918918921
 
< 0.1%
0.26362038661
 
< 0.1%
Other values (1018)1018
47.2%
ValueCountFrequency (%)
01128
52.3%
0.061603274911
 
< 0.1%
0.11223982381
 
< 0.1%
0.11264179911
 
< 0.1%
0.1803697581
 
< 0.1%
0.19221528111
 
< 0.1%
0.2130798231
 
< 0.1%
0.23369449761
 
< 0.1%
0.23816957321
 
< 0.1%
0.25676937441
 
< 0.1%
ValueCountFrequency (%)
38.028169011
< 0.1%
37.124762511
< 0.1%
31.024096391
< 0.1%
30.845771141
< 0.1%
30.473554741
< 0.1%
27.450980391
< 0.1%
25.027203481
< 0.1%
24.626865671
< 0.1%
23.819241981
< 0.1%
23.794712291
< 0.1%

feat_pct_hispanic
Real number (ℝ)

Zeros 

Distinct1469
Distinct (%)68.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.6365861
Minimum0
Maximum65.709832
Zeros672
Zeros (%)31.2%
Negative0
Negative (%)0.0%
Memory size17.0 KiB
2026-02-15T12:31:56.867445image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.6343835
Q34.1319117
95-th percentile15.574598
Maximum65.709832
Range65.709832
Interquartile range (IQR)4.1319117

Descriptive statistics

Standard deviation6.2048762
Coefficient of variation (CV)1.7062366
Kurtosis22.225163
Mean3.6365861
Median Absolute Deviation (MAD)1.6343835
Skewness3.9477002
Sum7840.4795
Variance38.500488
MonotonicityNot monotonic
2026-02-15T12:31:56.972478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0672
31.2%
1.0526315793
 
0.1%
1.7391304352
 
0.1%
1.5647226172
 
0.1%
0.90909090912
 
0.1%
1.9607843142
 
0.1%
2.2685469042
 
0.1%
1.4705882352
 
0.1%
4.0920716112
 
0.1%
1.0858835142
 
0.1%
Other values (1459)1465
67.9%
ValueCountFrequency (%)
0672
31.2%
0.26648900731
 
< 0.1%
0.33500837521
 
< 0.1%
0.34297963561
 
< 0.1%
0.35834738621
 
< 0.1%
0.35919540231
 
< 0.1%
0.37972834821
 
< 0.1%
0.39855072461
 
< 0.1%
0.4077194891
 
< 0.1%
0.42177914111
 
< 0.1%
ValueCountFrequency (%)
65.709831751
< 0.1%
61.91959231
< 0.1%
52.39234451
< 0.1%
49.73827821
< 0.1%
49.640122511
< 0.1%
47.781774581
< 0.1%
46.805658011
< 0.1%
46.678317311
< 0.1%
45.231228241
< 0.1%
44.789081891
< 0.1%

feat_pct_asian
Real number (ℝ)

High correlation  Zeros 

Distinct1167
Distinct (%)54.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.219399
Minimum0
Maximum45.762712
Zeros985
Zeros (%)45.7%
Negative0
Negative (%)0.0%
Memory size17.0 KiB
2026-02-15T12:31:57.069031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.63603023
Q31.663821
95-th percentile4.1546488
Maximum45.762712
Range45.762712
Interquartile range (IQR)1.663821

Descriptive statistics

Standard deviation2.4267418
Coefficient of variation (CV)1.990113
Kurtosis134.23954
Mean1.219399
Median Absolute Deviation (MAD)0.63603023
Skewness8.9355214
Sum2629.0242
Variance5.8890759
MonotonicityNot monotonic
2026-02-15T12:31:57.150942image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0985
45.7%
0.75711689882
 
0.1%
0.99585062242
 
0.1%
0.7267441862
 
0.1%
0.63569682152
 
0.1%
0.82987551872
 
0.1%
0.99085365851
 
< 0.1%
0.89167140961
 
< 0.1%
2.1705426361
 
< 0.1%
2.2127505211
 
< 0.1%
Other values (1157)1157
53.7%
ValueCountFrequency (%)
0985
45.7%
0.084226646251
 
< 0.1%
0.11826049561
 
< 0.1%
0.23387905111
 
< 0.1%
0.33574380171
 
< 0.1%
0.34182191081
 
< 0.1%
0.35499726931
 
< 0.1%
0.3757828811
 
< 0.1%
0.3774879891
 
< 0.1%
0.38623595511
 
< 0.1%
ValueCountFrequency (%)
45.762711861
< 0.1%
45.698849281
< 0.1%
37.464788731
< 0.1%
19.639858851
< 0.1%
19.178539631
< 0.1%
18.249258161
< 0.1%
15.241714171
< 0.1%
15.171714671
< 0.1%
14.79059121
< 0.1%
14.568081991
< 0.1%

feat_pct_aian
Real number (ℝ)

Zeros 

Distinct464
Distinct (%)21.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.97511703
Minimum0
Maximum78.571429
Zeros1691
Zeros (%)78.4%
Negative0
Negative (%)0.0%
Memory size17.0 KiB
2026-02-15T12:31:57.216005image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4.5445164
Maximum78.571429
Range78.571429
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.6704497
Coefficient of variation (CV)4.7896299
Kurtosis89.00186
Mean0.97511703
Median Absolute Deviation (MAD)0
Skewness8.3536943
Sum2102.3523
Variance21.8131
MonotonicityNot monotonic
2026-02-15T12:31:57.284023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
01691
78.4%
2.0560747662
 
0.1%
37.735849062
 
0.1%
5.6268509381
 
< 0.1%
0.50806933651
 
< 0.1%
3.2228360961
 
< 0.1%
0.46894983661
 
< 0.1%
1.6853932581
 
< 0.1%
1.1046817461
 
< 0.1%
0.44097693351
 
< 0.1%
Other values (454)454
 
21.1%
ValueCountFrequency (%)
01691
78.4%
0.010786209691
 
< 0.1%
0.012958791041
 
< 0.1%
0.019664112381
 
< 0.1%
0.032310770031
 
< 0.1%
0.035254996381
 
< 0.1%
0.043437156651
 
< 0.1%
0.044606883851
 
< 0.1%
0.049190547621
 
< 0.1%
0.050524188461
 
< 0.1%
ValueCountFrequency (%)
78.571428571
< 0.1%
58.791208791
< 0.1%
56.756756761
< 0.1%
53.097345131
< 0.1%
52.727272731
< 0.1%
43.751
< 0.1%
43.162393161
< 0.1%
37.931034481
< 0.1%
37.735849062
0.1%
37.603305791
< 0.1%

Interactions

2026-02-15T12:31:52.186915image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T12:31:34.723374image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T12:31:35.672654image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T12:31:36.822109image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T12:31:37.820968image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T12:31:39.300332image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T12:31:40.386064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T12:31:41.632106image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T12:31:42.695343image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T12:31:43.747885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T12:31:45.038164image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T12:31:46.101274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T12:31:47.084955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T12:31:48.100453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T12:31:49.725186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T12:31:50.866274image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T12:31:52.258933image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T12:31:34.787477image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T12:31:35.737870image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T12:31:36.867218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T12:31:37.886615image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T12:31:39.372707image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T12:31:40.448571image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T12:31:41.697845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T12:31:42.749506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T12:31:43.809427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T12:31:45.107223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T12:31:46.160278image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T12:31:47.146955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T12:31:48.176470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T12:31:49.784829image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T12:31:50.923735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T12:31:52.332721image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T12:31:34.850814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T12:31:35.821219image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T12:31:36.921718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T12:31:37.972794image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T12:31:39.466054image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T12:31:40.519954image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T12:31:41.754053image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T12:31:42.821359image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T12:31:43.872437image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T12:31:45.172240image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T12:31:46.219806image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T12:31:47.210493image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2026-02-15T12:31:41.569281image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T12:31:42.620081image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T12:31:43.685886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T12:31:44.972633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T12:31:46.040757image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T12:31:47.024447image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T12:31:48.037250image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T12:31:49.653512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T12:31:50.796778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2026-02-15T12:31:52.101606image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2026-02-15T12:31:57.333900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
avg_premium_before_APTCfeat_market_newnessfeat_net_attritionfeat_passive_renewalfeat_pct_FPL_100_150feat_pct_FPL_150_250feat_pct_FPL_250_400feat_pct_FPL_400_plusfeat_pct_age_55_overfeat_pct_aianfeat_pct_asianfeat_pct_blackfeat_pct_bronzefeat_pct_goldfeat_pct_hispanicfeat_subsidy_coverage_ratio
avg_premium_before_APTC1.000-0.0800.243-0.225-0.2500.1760.2660.1740.314-0.026-0.278-0.3130.1960.044-0.1850.232
feat_market_newness-0.0801.0000.085-0.130-0.2190.1840.2110.2630.153-0.0190.2470.003-0.0300.0710.194-0.302
feat_net_attrition0.2430.0851.000-0.513-0.4870.3090.4220.3370.1680.103-0.077-0.2180.2900.0480.020-0.326
feat_passive_renewal-0.225-0.130-0.5131.0000.669-0.347-0.641-0.556-0.165-0.0330.1280.453-0.256-0.135-0.0360.422
feat_pct_FPL_100_150-0.250-0.219-0.4870.6691.000-0.717-0.925-0.843-0.424-0.0650.1650.552-0.558-0.0240.2260.684
feat_pct_FPL_150_2500.1760.1840.309-0.347-0.7171.0000.6510.5060.3030.074-0.208-0.4520.384-0.027-0.216-0.390
feat_pct_FPL_250_4000.2660.2110.422-0.641-0.9250.6511.0000.8300.4240.029-0.163-0.5350.5540.062-0.177-0.623
feat_pct_FPL_400_plus0.1740.2630.337-0.556-0.8430.5060.8301.0000.4210.0520.000-0.3940.5110.059-0.086-0.660
feat_pct_age_55_over0.3140.1530.168-0.165-0.4240.3030.4240.4211.000-0.188-0.194-0.3810.300-0.127-0.267-0.399
feat_pct_aian-0.026-0.0190.103-0.033-0.0650.0740.0290.052-0.1881.0000.2050.0360.1020.0530.1940.010
feat_pct_asian-0.2780.247-0.0770.1280.165-0.208-0.1630.000-0.1940.2051.0000.512-0.0690.0620.498-0.078
feat_pct_black-0.3130.003-0.2180.4530.552-0.452-0.535-0.394-0.3810.0360.5121.000-0.235-0.0910.2450.260
feat_pct_bronze0.196-0.0300.290-0.256-0.5580.3840.5540.5110.3000.102-0.069-0.2351.000-0.312-0.236-0.402
feat_pct_gold0.0440.0710.048-0.135-0.024-0.0270.0620.059-0.1270.0530.062-0.091-0.3121.0000.3150.033
feat_pct_hispanic-0.1850.1940.020-0.0360.226-0.216-0.177-0.086-0.2670.1940.4980.245-0.2360.3151.0000.067
feat_subsidy_coverage_ratio0.232-0.302-0.3260.4220.684-0.390-0.623-0.660-0.3990.010-0.0780.260-0.4020.0330.0671.000

Missing values

2026-02-15T12:31:53.867380image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2026-02-15T12:31:53.984269image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

feat_pct_bronzefeat_pct_goldfeat_pct_FPL_100_150feat_pct_FPL_150_250feat_pct_FPL_250_400feat_pct_FPL_400_plusavg_premium_before_APTCfeat_subsidy_coverage_ratiofeat_net_attritionfeat_market_newnessfeat_passive_renewalfeat_pct_age_55_overfeat_pct_blackfeat_pct_hispanicfeat_pct_asianfeat_pct_aian
016.7978293.98914549.25373130.47489812.1845324.5861606650.8796997.26732418.31750346.27907021.62822315.4138402.0624155.4274080.0
127.5609013.51955748.67440328.44847514.5393076.0708526610.8426634.58148016.62568244.64261025.7972804.3956043.5349271.8020440.0
220.1981710.00000072.98018318.8643294.9923781.9054886690.9103140.00000011.01372058.88651023.24695111.6615850.0000000.9908540.0
332.6633170.00000067.78894523.1155785.5778891.9597996560.9253050.00000013.86934761.78529820.4020106.0804020.7537690.0000000.0
427.0726622.63707160.25422125.4031499.5617532.5991276780.9056053.00331413.90628054.64962523.8095240.8537283.5287420.8916710.0
519.1709840.00000079.08031116.6450782.5906740.0000006490.9060090.00000012.56476765.55555620.40155417.2279790.0000000.0000000.0
612.3255813.10077556.82170526.97674410.2325583.0232566920.8973991.00896914.57364348.18511823.48837222.6356590.9302332.1705430.0
716.2782261.74118168.74943320.9213756.4568792.0313786290.9316382.16169114.95420355.13968922.4721146.4568791.4509842.2127510.0
818.1461410.76621275.38777818.0713894.0553170.0000006180.9239482.46849813.99738459.84354618.2582698.9889742.6163330.6167070.0
926.0901162.28924463.98982622.7107568.5029072.9069777070.9236210.00000012.35465154.06301828.3793601.9985470.6904070.7267440.0
feat_pct_bronzefeat_pct_goldfeat_pct_FPL_100_150feat_pct_FPL_150_250feat_pct_FPL_250_400feat_pct_FPL_400_plusavg_premium_before_APTCfeat_subsidy_coverage_ratiofeat_net_attritionfeat_market_newnessfeat_passive_renewalfeat_pct_age_55_overfeat_pct_blackfeat_pct_hispanicfeat_pct_asianfeat_pct_aian
214616.66666760.55555625.00000027.77777832.77777813.88888911550.91342013.34277517.77777825.00000035.0000000.00.0000000.0000000.000000
214723.72140853.97070217.52762835.72346427.08815218.52994110170.88692211.34456416.67951725.35471926.5741450.03.8807500.8481110.565407
214825.77777848.88888919.70370435.11111129.33333315.11111110650.90704212.96862316.88888929.76827131.2592590.05.0370370.0000000.000000
214926.84469453.44342916.76036531.44764630.88545319.2902329880.86639712.55248718.55235427.99827424.8770200.03.4082921.7919890.000000
215022.48062055.98621915.67614136.00344528.07924219.37984510250.88097612.30944120.15503928.47896426.9595180.03.9621020.0000000.000000
215119.17808250.08561625.55650733.17637022.81678116.30993211110.90099014.91004423.67294532.08076332.4486300.08.4760272.1832190.856164
215230.47808854.4678439.96015920.66021629.39669934.8321009430.71898215.14685517.95674430.41970219.9203190.04.8947071.2521340.000000
215318.92255950.43771023.90572435.89225623.36700315.0841759950.8964827.50276217.91245824.77440524.2424240.03.5690241.0101010.000000
215424.64589251.41643122.66289033.71104828.75354114.16430610510.9172229.35028919.97167125.84070828.4702550.08.3569410.0000000.000000
215521.92029051.08695721.55797134.60144925.54347816.66666711570.8971488.58117219.74637723.47629838.2246380.02.3550720.0000000.000000